CN113221452B - Office space temperature prediction system based on distributed optical fiber - Google Patents
Office space temperature prediction system based on distributed optical fiber Download PDFInfo
- Publication number
- CN113221452B CN113221452B CN202110475169.2A CN202110475169A CN113221452B CN 113221452 B CN113221452 B CN 113221452B CN 202110475169 A CN202110475169 A CN 202110475169A CN 113221452 B CN113221452 B CN 113221452B
- Authority
- CN
- China
- Prior art keywords
- temperature
- optical fiber
- module
- internal disturbance
- height
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Air Conditioning Control Device (AREA)
- Measuring Temperature Or Quantity Of Heat (AREA)
Abstract
The invention discloses an office space temperature prediction system based on a distributed optical fiber, which comprises an optical fiber temperature correction module, a human factor internal disturbance correction module and a temperature prediction module; the optical fiber temperature correction module is used for determining the real-time indoor air temperature as the input of the temperature prediction module; the artificial internal disturbance correction module is used for determining the position of the indoor internal disturbance and determining the influence of the indoor internal disturbance on the air temperature at the ceiling height and the temperature field at the required indoor height within unit regulation and control time as the input of the temperature prediction module; the temperature prediction module is used to finalize the temperature distribution at the desired altitude.
Description
Technical Field
The invention relates to the field of indoor temperature prediction methods, in particular to an office space temperature prediction system based on distributed optical fibers.
Background
As one of the most common building types, the office building has important significance in building an energy-saving and comfortable office environment. The individualized adjustment of the air conditioner, as a novel indoor environment regulation and control method, can effectively take energy saving and comfort into consideration, but the implementation of the method depends on high-precision indoor temperature field distribution. At present, a point type temperature sensor is mainly used in a common indoor temperature acquisition mode, if fine indoor temperature field distribution needs to be acquired, massive temperature sensors are needed, and the manufacturing cost is too high. In addition, the existing temperature prediction method is mainly based on historical time sequence data or air conditioning equipment operation parameters, and the influence of real-time internal disturbance change on indoor temperature is not considered.
The most recent prior patents and papers to date have the following:
1) a data center distributed optical fiber sensing monitoring system (103701898A) comprises a distributed optical fiber temperature measurement host and a monitoring server. The distributed optical fiber temperature measurement host is connected with the monitoring server through a network, the temperature measurement optical fibers are laid to the front door and the back door of the cabinet, indoor space temperature distribution information is measured in real time, and accurate measurement of a data center microenvironment is achieved. And uploading the data measured by the optical fiber to a monitoring server, and analyzing and storing the temperature data.
The invention aims to reduce the number of electronic temperature sensors applied to data center machine room temperature monitoring, reduce the installation difficulty of a temperature monitoring system, and improve the granularity and accuracy of an environment monitoring system, and provides a real-time data center machine room temperature monitoring system. However, the method mainly solves the problem of temperature monitoring in an indoor scene, lacks the function of real-time prediction of the indoor temperature field, and lacks the steps of calibration, abnormal value screening and the like for the measured temperature.
2) A temperature prediction method and system for intelligent greenhouses (112418498A), the method comprises two steps of greenhouse environment data acquisition and greenhouse temperature prediction. The environmental data acquisition comprises temperature data, humidity data, air pressure data, wind direction data, wind speed data, rainfall data, illumination data and carbon dioxide data. The greenhouse temperature prediction model comprises an error back propagation neural network, a gradient descent tree model, an elastic network regression model and a Light GBM model. After the environmental parameters are collected, a machine learning model is trained, the indoor preset temperature is predicted, and the effect of controlling the temperature of the greenhouse in advance is achieved.
The invention aims to provide an accurate basis for greenhouse temperature regulation and control and reduce yield reduction caused by poor greenhouse temperature control, and provides a temperature prediction method and system for an intelligent greenhouse. However, the invention mainly solves the problem of temperature prediction in the greenhouse, and the greenhouse has no irregular internal heat source change and is not applicable to the problem of office space temperature prediction.
3) An indoor temperature prediction method (110298487A) that meets user personalized needs. Aiming at the personalized heat comfort requirement of a user, the method uses PMV indexes to represent a human body heat comfort prediction model, and uses a deep learning method to predict the indoor temperature. The indoor temperature prediction model comprises four parts of data acquisition, data preprocessing, neural network building and model training prediction, and the conditions of different seasons, different application scenes and the like are considered.
Therefore, the technology for regulating and predicting the personalized demand by using the application scene is lacked in the prior art, and indoor internal disturbance change is not considered. No prediction can be made for the indoor temperature field. The technical scheme in the aspect of the office space temperature field prediction method based on distributed optical fiber temperature measurement is needed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an office space temperature prediction system based on a distributed optical fiber, which can effectively utilize the temperature measurement result of the distributed optical fiber, combines indoor personnel distribution information provided by a monitoring video, and considers the influence of internal disturbance on indoor temperature. The method improves the prediction accuracy of the temperature field of the office space, effectively solves the problem of regulation time lag caused by temperature information lag at present, and can effectively guide the operation and maintenance of the air conditioner applied to the office space.
The purpose of the invention is realized by the following technical scheme:
an office space temperature prediction system based on a distributed optical fiber comprises an optical fiber temperature correction module, a human factor internal disturbance correction module and a temperature prediction module;
the optical fiber temperature correction module is used for determining the real-time indoor air temperature as the input of the temperature prediction module;
the human factor internal disturbance correction module is used for determining the position of the indoor internal disturbance and determining the influence of the indoor internal disturbance on the air temperature at the ceiling height and the temperature field at the required indoor height within unit regulation and control time as the input of the temperature prediction module;
the temperature prediction module is used for determining the temperature distribution at the final required height;
in the optical fiber temperature correction module, optical fibers are uniformly arranged at the side wall and the ceiling according to the required spatial resolution, temperature information of measuring points at the height of the ceiling and the side wall is obtained by using a distributed optical fiber temperature measuring host, and then the temperature measured by the optical fibers is corrected by using a temperature correction mathematical model;
the mathematical model for temperature correction is shown in formula 1, wherein x is the measured temperature of the optical fiber, and Y is the corrected measured temperature of the optical fiber.
Y＝-105.4928+27.3917x-2.22707x 2 +0.07845x 3 -0.000994034x 4 (1)
The man-caused internal disturbance correction module comprises a man distribution identification module and a man-caused internal disturbance influence quantification module, wherein the man distribution identification module is used for providing man distribution information, regularly acquiring image information by using a monitoring camera in an office space, and extracting the position and quantity information of the man in the monitored office space by using a target detection method; the human factor internal disturbance influence quantification module utilizes CFD simulation software and comprises quantification results of influences of different seasons and different personnel distribution on temperatures at different heights in a room; extracting a corresponding temperature influence quantization matrix result by utilizing real-time personnel information provided by a personnel distribution identification module, and using the result of the temperature influence quantization matrix result as the result correction of a subsequent temperature prediction module;
the temperature prediction module subtracts a ceiling height temperature influence quantization matrix provided by the human factor internal disturbance correction module from the temperature data provided by the optical fiber temperature correction module to obtain the temperature of the ceiling under the condition of no human factor internal disturbance, and predicts the temperature under the condition of no human factor internal disturbance at the required height by using a support vector machine regression algorithm; and finally, adding the temperature influence quantization matrix at the required height provided by the human factor internal disturbance correction module to the temperature prediction result at the required height without human factor internal disturbance to obtain the final required temperature field prediction result.
Further, the temperature correction mathematical model is used for synchronously monitoring the positions of the temperature and humidity sensor and the distributed optical fiber sensor, and establishing the temperature correction mathematical model of the distributed optical fiber sensor by using synchronous monitoring data of corresponding point positions; calculating the difference between any two temperature points which are closest to the temperature points along the arrangement direction of the optical fiber and the variation rate of historical temperature data of the previous 5 times of the temperature points for all the corrected temperature points, and if the difference is overlarge, treating the corresponding corrected temperature points as temperature abnormal points; the temperature data obtained by the optical fiber temperature correction module is used as the input of the following steps.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. aiming at the problem that the indoor temperature field is difficult to acquire, the temperature data of the ceiling and the height of the side wall are acquired by using the distributed optical fiber temperature measuring system, so that the difficulty in acquiring the indoor temperature field is simplified.
2. Aiming at the problem that the temperature monitoring of the distributed optical fiber temperature measuring system is unstable, the result of the distributed optical fiber temperature measuring system is corrected by using the optical fiber temperature correcting module, and the indoor temperature field acquisition precision is improved.
3. Aiming at the problem that the prediction accuracy and the calculation time of the indoor temperature field are difficult to balance, a machine learning method is utilized to establish an indoor temperature field prediction model, so that the prediction accuracy is ensured, and the prediction time is shortened.
4. Aiming at the problem that the internal disturbance of the indoor temperature field is difficult to quantify in the prediction of the human factors, the internal disturbance correction module of the human factors is used for correcting the indoor temperature prediction model, the influence of the internal disturbance of the human factors on the indoor temperature field is quantified, and the prediction precision is improved.
Drawings
FIG. 1 is a schematic workflow diagram of the system of the present invention.
Fig. 2 shows the arrangement of seats in an office space according to the present embodiment.
Fig. 3a and 3b are schematic diagrams of the quantization matrix of the temperature influence at the ceiling height corresponding to the quantization module of the influence of the human internal disturbance in the present embodiment.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides an office temperature field prediction system based on a distributed optical fiber, which comprises an optical fiber temperature correction module, a human factor internal disturbance correction module and a temperature prediction module. The work flow of the system is shown in the figure 1,.
The optical fiber temperature correction module is used for determining real-time air temperatures at the positions of the ceiling height, the specified height of the side wall and the like and used as the input of the indoor temperature prediction module; the method comprises the steps of firstly, uniformly arranging optical fibers at the side wall and the ceiling according to the required spatial resolution, obtaining temperature information of a series of measuring points at the height of the ceiling and the side wall by using a distributed optical fiber temperature measuring host, and then correcting the temperature measured by the optical fibers by using a proposed temperature correction mathematical model. The mathematical model for temperature correction is obtained by using the formula Y-105.4928 +27.3917x-2.22707x 2 +0.07845x 3 -0.000994034x 4 . And calculating the difference between any two temperature points with the closest distance in the arrangement direction of the optical fiber and the variation rate of the historical temperature data 5 times before the point for all the corrected temperature points, and treating the temperature points as temperature abnormal points if the difference is too large. The temperature data obtained by the optical fiber temperature correction module is used as the input of the following steps.
The man-caused internal disturbance correction module is used for determining the position of the indoor internal disturbance, determining the influence of the internal disturbance on the air temperature at the ceiling height and the temperature field at the required indoor height within unit regulation and control time, and taking the influence as the input of the indoor temperature prediction module; the human factor internal disturbance correction module mainly comprises a personnel distribution identification module and a human factor internal disturbance influence quantification module, wherein the personnel distribution identification module is used for providing personnel distribution information, a monitoring camera in an office space is used for collecting image information at regular time, and the position and quantity information of personnel in the monitored office space is extracted by using a target detection method. The human factor internal disturbance influence quantification module utilizes CFD simulation software and comprises quantification results of influences of different seasons and different personnel distribution on temperatures at different heights in a room. And extracting a corresponding temperature influence quantization matrix result by utilizing real-time personnel information provided by the personnel distribution identification module for correcting a subsequent temperature prediction module result.
The indoor temperature prediction module subtracts a ceiling height temperature influence quantization matrix provided by the human-caused internal disturbance correction module from temperature data provided by the optical fiber temperature correction module to obtain the temperature of the ceiling under the condition of no human-caused internal disturbance, and predicts the temperature under the condition of no human-caused internal disturbance at the required height by using a support vector machine regression algorithm. And finally, adding the temperature influence quantization matrix at the required height provided by the internal disturbance correction module to the temperature prediction result at the required height without internal disturbance to obtain the final required temperature field prediction result.
Specifically, the present embodiment selects a typical office space to illustrate the embodiments of the invention. The number of rows of seats in the selected office space is four, and the specific arrangement form is shown in fig. 2. Assuming that a single interval of personnel data acquisition and temperature regulation is set to be 15 minutes, only the first row is fully seated before 15 minutes, and a prediction flow of a temperature field 15 minutes after the desktop height (0.8m) is given.
In the first step, the temperature at the current ceiling height and the temperature at the side wall height are obtained based on the optical fiber temperature correction module, and the temperature correction mathematical model Y mentioned above is adopted for the measurement result as-105.4928 +27.3917x-2.22707x 2 +0.07845x 3 -0.000994034x 4 And performing real-time online correction, and performing abnormal value detection on the correction result to obtain the temperature of the measurement points at the ceiling height and the side wall height.
And secondly, acquiring that the personnel are fully seated in the first row by utilizing a personnel distribution identification module in the personnel internal disturbance correction module, and finding a corresponding temperature influence quantization matrix at the desktop height from the personnel internal disturbance influence quantization module, wherein the matrix is shown in fig. 3a and 3 b. And referring to the previous 15 minutes of personnel information, finding a corresponding temperature influence quantification matrix at the ceiling height from the personnel internal disturbance influence quantification module, wherein the matrix is shown in fig. 3a and 3 b. And subtracting the influence of the human factor internal disturbance on the temperature of the ceiling before 15 minutes provided by the human factor internal disturbance correction module from the temperature of the ceiling obtained in the first step to obtain the temperature of the height of the ceiling under the unmanned condition.
And thirdly, obtaining the temperature of the height of the desktop under the unmanned condition at the current moment by using the SVR algorithm in the temperature prediction module and taking the temperature of the height of the ceiling under the unmanned condition obtained in the second step and the temperature of the side wall obtained in the first step as input.
And fourthly, after the temperature at the desktop height under the current unmanned condition is obtained in the third step, adding the temperature to the temperature at the desktop height due to internal disturbance (the 1 st row full sitting condition) after 15 minutes obtained in the second step, namely obtaining the temperature at the desktop height after 15 minutes, wherein all temperature points are linked based on visual processing and an air conditioner intelligent operation control system so as to realize that air conditioner operation and maintenance personnel regulate and control air conditioner systems in different areas.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make various changes in form and details without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. An office space temperature prediction system based on a distributed optical fiber is characterized by comprising an optical fiber temperature correction module, a human factor internal disturbance correction module and a temperature prediction module;
the optical fiber temperature correction module is used for determining the real-time indoor air temperature as the input of the temperature prediction module;
the human factor internal disturbance correction module is used for determining the position of the indoor internal disturbance and determining the influence of the indoor internal disturbance on the air temperature at the ceiling height and the temperature field at the required indoor height within unit regulation time as the input of the temperature prediction module;
the temperature prediction module is used for determining the temperature distribution at the final required height;
in the optical fiber temperature correction module, optical fibers are uniformly arranged at the side wall and the ceiling according to the required spatial resolution, temperature information of measuring points at the height of the ceiling and the side wall is obtained by using a distributed optical fiber temperature measuring host, and then the temperature measured by the optical fibers is corrected by using a temperature correction mathematical model; the temperature correction mathematical model is as follows: -105.4928+27.3917x-2.22707x 2 +0.07845x 3 -0.000994034x 4 Wherein x is the measured temperature of the optical fiber, and Y is the corrected measured temperature of the optical fiber; performing position synchronous monitoring by using a temperature and humidity sensor and a distributed optical fiber sensor, and establishing a temperature correction mathematical model of the distributed optical fiber sensor by using synchronous monitoring data of corresponding point positions; calculating the difference between any two temperature points which are closest to the temperature points along the arrangement direction of the optical fiber and the variation rate of historical temperature data of the previous 5 times of the temperature points for all the corrected temperature points, and if the difference is overlarge, treating the corresponding corrected temperature points as temperature abnormal points; the temperature data obtained by the optical fiber temperature correction module is used as the following stepsInputting;
the man-caused internal disturbance correction module comprises a man distribution identification module and a man-caused internal disturbance influence quantification module, wherein the man distribution identification module is used for providing man distribution information, regularly acquiring image information by using a monitoring camera in an office space, and extracting the position and quantity information of the man in the monitored office space by using a target detection method; the human factor internal disturbance influence quantification module utilizes CFD simulation software and comprises quantification results of influences of different seasons and different personnel distribution on temperatures at different heights in a room; extracting a corresponding temperature influence quantization matrix result by utilizing real-time personnel information provided by a personnel distribution identification module, and using the result as the result correction of a subsequent temperature prediction module;
the temperature prediction module subtracts a ceiling height temperature influence quantization matrix provided by the human factor internal disturbance correction module from the temperature data provided by the optical fiber temperature correction module to obtain the temperature of the ceiling under the condition of no human factor internal disturbance, and predicts the temperature under the condition of no human factor internal disturbance at the required height by using a support vector machine regression algorithm; and finally, adding the temperature influence quantization matrix at the required height provided by the human factor internal disturbance correction module to the temperature prediction result at the required height without human factor internal disturbance to obtain the final required temperature field prediction result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110475169.2A CN113221452B (en) | 2021-04-29 | 2021-04-29 | Office space temperature prediction system based on distributed optical fiber |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110475169.2A CN113221452B (en) | 2021-04-29 | 2021-04-29 | Office space temperature prediction system based on distributed optical fiber |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113221452A CN113221452A (en) | 2021-08-06 |
| CN113221452B true CN113221452B (en) | 2022-09-09 |
Family
ID=77090341
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110475169.2A Active CN113221452B (en) | 2021-04-29 | 2021-04-29 | Office space temperature prediction system based on distributed optical fiber |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113221452B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115808211A (en) * | 2023-02-06 | 2023-03-17 | 天津大学 | Public building temperature and thermal comfort monitoring and predicting system |
| CN116305476B (en) * | 2023-03-24 | 2024-09-17 | 天津大学 | Rapid prediction method for space-time dynamic distribution of indoor temperature field |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110641494A (en) * | 2019-07-17 | 2020-01-03 | 中车青岛四方机车车辆股份有限公司 | Computer device and method for controlling temperature distribution of internal space of rail transit vehicle |
| CN112513567A (en) * | 2018-07-31 | 2021-03-16 | 古河电气工业株式会社 | Cable, shape sensing system of cable, sensing system and sensing method of cable shape |
| CN112595434A (en) * | 2020-12-30 | 2021-04-02 | 广州旭杰电子有限公司 | Distributed optical fiber temperature measurement intelligent monitoring system |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104812324B (en) * | 2012-11-19 | 2018-03-30 | 皇家飞利浦有限公司 | temperature distribution determining device |
| US9879874B2 (en) * | 2013-04-15 | 2018-01-30 | Mitsubishi Electric Corporation | Air-conditioning system control apparatus |
| CN103701898A (en) * | 2013-12-26 | 2014-04-02 | 上海邮电设计咨询研究院有限公司 | Data center distributed optical fiber sensing monitoring system |
| CN105972765B (en) * | 2016-05-25 | 2019-07-12 | 深圳先进技术研究院 | A kind of intelligent air condition and its temperature control method |
| JP6938969B2 (en) * | 2017-03-07 | 2021-09-22 | 富士フイルムビジネスイノベーション株式会社 | Environmental measurement systems and programs |
| CN111272293A (en) * | 2019-12-31 | 2020-06-12 | 扬州海通电子科技有限公司 | Heat distribution monitoring system and detection method thereof |
| CN111444646B (en) * | 2020-03-11 | 2022-06-10 | 天津大学 | Method for researching influence of building space size on air temperature vertical distribution mode |
| CN111459214A (en) * | 2020-04-13 | 2020-07-28 | 安徽天达网络科技有限公司 | Control management system and method based on intelligent environment |
-
2021
- 2021-04-29 CN CN202110475169.2A patent/CN113221452B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112513567A (en) * | 2018-07-31 | 2021-03-16 | 古河电气工业株式会社 | Cable, shape sensing system of cable, sensing system and sensing method of cable shape |
| CN110641494A (en) * | 2019-07-17 | 2020-01-03 | 中车青岛四方机车车辆股份有限公司 | Computer device and method for controlling temperature distribution of internal space of rail transit vehicle |
| CN112595434A (en) * | 2020-12-30 | 2021-04-02 | 广州旭杰电子有限公司 | Distributed optical fiber temperature measurement intelligent monitoring system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113221452A (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102380397B1 (en) | METHOD FOR MANAGING SMART BUILDING USING IoT SENSOR AND ARTIFICIAL INTELIGENCE | |
| CN113221452B (en) | Office space temperature prediction system based on distributed optical fiber | |
| CN115470566B (en) | Intelligent building energy consumption control method and system based on BIM | |
| CN108388291A (en) | A kind of greenhouse cluster environment regulation and control method and system | |
| EP3078919A1 (en) | Apparatus and method for controlling comfort temperature of air conditioning device or air conditioning system | |
| CN109948824B (en) | Method for predicting heat load of heating power station by using pattern recognition technology | |
| CN108105844B (en) | Intelligent heat exchange station control method based on indoor and outdoor temperature combined compensation | |
| CN102859550A (en) | Energy demand prediction device and method | |
| CN105190191A (en) | Energy saving heating, ventilation, air conditioning control system | |
| CN107720469A (en) | A kind of method and system that elevator floor is measured by temperature and air pressure sensor | |
| CN108710947A (en) | A kind of smart home machine learning system design method based on LSTM | |
| CN114200839B (en) | Intelligent office building energy consumption control model for dynamic monitoring of coupling environment behaviors | |
| CN115808211A (en) | Public building temperature and thermal comfort monitoring and predicting system | |
| CN109523134B (en) | Quantitative assessment method for time shifting capability of distributed electric heating load based on measured data and modeling system thereof | |
| JP2020008534A (en) | Insulation performance diagnostic device | |
| CN107192098A (en) | Hospital ventilation regulates and controls method and regulator control system | |
| CN116542502B (en) | Building construction period optimization system and method based on convolutional neural network | |
| CN115342409B (en) | Control and regulation method and system for heating system | |
| Kciuk et al. | Design and modeling of intelligent building office and thermal comfort based on probabilistic neural network | |
| CN115660325B (en) | Power grid peak regulation capacity quantization method and system | |
| CN112132428B (en) | Predictive regulation and control decision method for steam distribution station of steam heat supply network based on big data | |
| CN118192479B (en) | Factory workshop digital twin model correction method and system based on data acquisition | |
| CN118505069B (en) | Intelligent control system for concrete product maintenance | |
| CN117391312B (en) | Intelligent alarm processing method and system for intelligent building | |
| CN117075491B (en) | Intelligent household health management system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |